Recent text-conditional image manipulation methodologies show promising results [1]}, [2]}, [3]}, [4]}, [5]}. Those works use a powerful joint multi-modal embedding space which is trained on a large scale image-text pairs. These text-based prior works provide a powerful tool for image manipulation.
In particular, StyleCLIP [6]}, one of the best text-guided image manipulation method, considered leveraging the representational power of Contrastive Language-Image Pre-training (CLIP) [7]} models to produce text-relevant manipulations with given text input.
StyleCLIP also maintains high-quality image generation ability using StyleGAN [8]} while allowing the style insertion of text semantics into the image. Both \(\mathcal {W}\)  space and extended latent space \(\mathcal {W}+\) , the intermediate latent space of StyleGAN, are suitable for manipulation because they disentangle the feature in the representation spaces.
